Anchored causal inference in the presence of measurement error
Author(s)
Saeed, B; Belyaeva, A; Wang, Y; Uhler, C
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We consider the problem of learning a causal graph in the presence of measurement error. This setting is for example common in genomics, where gene expression is corrupted through the measurement process. We develop a provably consistent procedure for estimating the causal structure in a linear Gaussian structural equation model from corrupted observations on its nodes, under a variety of measurement error models. Namely, we provide an estimator based on the method-of-moments and an associated test which can be used in conjunction with constraint-based causal structure discovery algorithms. We prove asymptotic consistency of the procedure and also discuss finite-sample considerations. We demonstrate our method's performance through simulations and on real data, where we recover the underlying gene regulatory network from zero-inflated single-cell RNA-seq data.
Date issued
2020-01-01Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Institute for Data, Systems, and SocietyJournal
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020
Citation
Saeed, B, Belyaeva, A, Wang, Y and Uhler, C. 2020. "Anchored causal inference in the presence of measurement error." Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020, 124.
Version: Final published version